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  3. Bayesian Decision Analysis
  • Cited by 68
Publisher:
Cambridge University Press
Online publication date:
June 2012
Print publication year:
2010
Online ISBN:
9780511779237
DOI:
https://doi.org/10.1017/CBO9780511779237
Subjects:
Organisation Studies, Optimization, OR and risk, Statistics and Probability, Management
Information

Book description

Bayesian decision analysis supports principled decision making in complex domains. This textbook takes the reader from a formal analysis of simple decision problems to a careful analysis of the sometimes very complex and data rich structures confronted by practitioners. The book contains basic material on subjective probability theory and multi-attribute utility theory, event and decision trees, Bayesian networks, influence diagrams and causal Bayesian networks. The author demonstrates when and how the theory can be successfully applied to a given decision problem, how data can be sampled and expert judgements elicited to support this analysis, and when and how an effective Bayesian decision analysis can be implemented. Evolving from a third-year undergraduate course taught by the author over many years, all of the material in this book will be accessible to a student who has completed introductory courses in probability and mathematical statistics.

Reviews

‘The author presents a good set of solved exercises, which serve for illustration, and a large set of proposed exercises are suggested. I recommend this book for professional and advanced students in statistics, operations research, computer science, artificial intelligence, cognitive sciences and different branches of engineering.’

Narciso Bouza Herrera Source: Zentralblatt MATH

'… an excellent resource for students at final year undergraduate level or higher, and for anyone researching issues of complex decision-making.'

Source: Mathematics Today

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